Application of genetic algorithm in integrated setup planning and operation sequencing

被引:0
|
作者
Kafashi, Sajad [1 ]
Shakeri, Mohsen [1 ]
机构
[1] Babol Univ Technol Babol, Dept Mech Engn, Mazandaran, Iran
关键词
process planning; setup planning; operation sequencing; integerated setup planning and operation sequencing (ISOS); genetic algorithm; OPTIMIZATION;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Process planning is an essential component for linking design and manufacturing process. Setup planning and operation sequencing is two main tasks in process planning. Many researches solved these two problems separately. Considering the fact that the two functions are complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved economically and competitively. This paper present a generative system and genetic algorithm (GA) approach to process plan the given part. The proposed approach and optimization methodology analyses the TAD (tool approach direction), tolerance relation between features and feature precedence relations to generate all possible setups and operations using workshop resource database. Based on these technological constraints the GA algorithm approach, which adopts the feature-based representation, optimizes the setup plan and sequence of operations using cost indices. Case study show that the developed system can generate satisfactory results in optimizing the setup planning and operation sequencing simultaneously in feasible condition.
引用
收藏
页码:1413 / 1418
页数:6
相关论文
共 50 条
  • [31] An active learning genetic algorithm for integrated process planning and scheduling
    Li, Xinyu
    Gao, Liang
    Shao, Xinyu
    EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (08) : 6683 - 6691
  • [32] Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling
    Xu Zhang
    Zhixue Liao
    Lichao Ma
    Jin Yao
    Journal of Intelligent Manufacturing, 2022, 33 : 223 - 246
  • [33] Optimizing the integrated production and maintenance planning using genetic algorithm
    Ettaye, Ghita
    El Barkany, Abdellah
    Jabri, Abdelouahhab
    El Khalfi, Ahmed
    INTERNATIONAL JOURNAL OF ENGINEERING BUSINESS MANAGEMENT, 2018, 10
  • [34] Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling
    Zhang, Xu
    Liao, Zhixue
    Ma, Lichao
    Yao, Jin
    JOURNAL OF INTELLIGENT MANUFACTURING, 2022, 33 (01) : 223 - 246
  • [35] Application of Interactive Genetic Algorithm in Landscape Planning and Design
    Li, Boyang
    Sharma, Ashutosh
    INFORMATICA-AN INTERNATIONAL JOURNAL OF COMPUTING AND INFORMATICS, 2022, 46 (03): : 365 - 372
  • [36] Application of Genetic Algorithm in optimal robotic path planning
    Xu, Sendren Shen-Dong
    Wu, Ya-Po
    Chang, Teng-Chang
    INNOVATION, COMMUNICATION AND ENGINEERING, 2014, : 325 - 328
  • [37] Application of Genetic Algorithm to Spatial Distribution in Urban Planning
    Hu Xin
    Zhang Zhi-xia
    2008 IEEE INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING WORKSHOP PROCEEDINGS, VOLS 1 AND 2, 2008, : 1026 - +
  • [38] Application of quantum genetic algorithm in logistics distribution planning
    Wang Xihuai
    Ying, Yang
    Xiao Jianmei
    PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 5, 2007, : 759 - +
  • [39] Application of the improved genetic algorithm in robot path planning
    Wang Rui
    Wang Jinguo
    Wang Na
    PROCEEDINGS OF THE 2015 JOINT INTERNATIONAL MECHANICAL, ELECTRONIC AND INFORMATION TECHNOLOGY CONFERENCE (JIMET 2015), 2015, 10 : 1081 - 1084
  • [40] Research on genetic algorithm optimization for agricultural machinery operation path planning
    Song, Xiuming
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)